Modeling Taxi Trip Demand by Time of Day in New York City

Yang, Ci; Gonzales, Eric J.

doi:10.3141/2429-12

Cited by 64 publications

(37 citation statements)

References 11 publications

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“…In recent years, the New York City Taxi and Limousine Commission (NYCTLC (2016)) has maintained a complete public record of yellow cab rides within the city. The database contains relevant metadata such as the origin, destination, fare, distance and time traveled for over 170 million rides per year, and has been exploited for a variety of purposes, as shown in Yang (2015). Despite the data's size and availability, however, it has not been used very much for travel time estimation.…”

Section: The Need For a Generalized Approach To Travel Time Estimationmentioning

confidence: 99%

Travel Time Estimation in the Age of Big Data

Bertsimas

Delarue

Jaillet

et al. 2019

Operations Research

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Twenty-first century urban planners have identified the understanding of complex city traffic patterns as a major priority, leading to a sharp increase in the amount and the diversity of traffic data being collected. For instance, taxi companies in an increasing number of major cities have started recording metadata for every individual car ride, such as its origin, destination and travel time. In this paper, we show that we can leverage network optimization insights to extract accurate travel time estimations from such origin-destination data, using information from a large number of taxi trips to reconstruct the traffic patterns in an entire city. We develop a method that tractably exploits origin-destination data, which, because of its optimization framework, could also take advantage of other sources of traffic information. Using synthetic data, we establish the robustness of our algorithm to high variance data, and the interpretability of its results. We then use hundreds of thousands of taxi travel times observations in Manhattan to show that our algorithm can provide insights about urban traffic patterns on different scales and accurate travel time estimations throughout the network.

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Section: The Need For a Generalized Approach To Travel Time Estimationmentioning

confidence: 99%

Travel Time Estimation in the Age of Big Data

Bertsimas

Delarue

Jaillet

et al. 2019

Operations Research

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“…This massive dataset contains all ride-sharing and taxi trips in NYC, starting from 2009, for a total of more than 1 billion rides. A comprehensive description of this data-set is available in [33]. It has been used for vehicle routing decisions in [26,35,29].…”

Section: Related Workmentioning

confidence: 99%

Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications

Bertsimas

Jaillet

Martin

2019

Operations Research

175

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With the emergence of ride-sharing companies that offer transportation on demand at a large scale and the increasing availability of corresponding demand datasets, new challenges arise to develop routing optimization algorithms that can solve massive problems in real time. In this paper, we develop an optimization framework, coupled with a novel and generalizable backbone algorithm, that allows us to dispatch in real time thousands of taxis serving more than 25,000 customers per hour. We provide evidence from historical simulations using New York City routing network and yellow cabs data to show that our algorithms improve upon the performance of existing heuristics in such real-world settings.

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“…Typically, among vehicle GPS data, floating car data (FCD) are likely to pave a new way for understanding individual-level travel behaviour and bring transport research to a big data era. In the last few years, FCD has been widely used in transport research, including the modelling of passenger demand [9][10][11], estimation of gas emissions [12][13][14], analysis of drivers' behaviour [15][16][17], and estimation of travel time [18][19][20]. There are several taxi FCD datasets open to the public, including Beijing datasets, New York City datasets, and so forth, and a number of studies have been undertaken around these open taxi FCD datasets [10,15].…”

Section: Introductionmentioning

confidence: 99%

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

Sun

Ren

Sun

2020

IJGI

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Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston.

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Modeling Taxi Trip Demand by Time of Day in New York City

Cited by 64 publications

References 11 publications

Travel Time Estimation in the Age of Big Data

Travel Time Estimation in the Age of Big Data

Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

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